Adapting playback settings based on change history

ABSTRACT

Techniques discussed herein improve a user&#39;s playback experience of a multimedia file by automatically adjusting playback settings based on change history data stored in one or more profiles. A system is configured to detect and collect information regarding changes to playback settings made by one or more users, and/or made in response to input from one or more users, during presentation of a media title, such as a song and/or movie, on one or more computers. This information is processed by a backend process on one or more server computers to determine predicted preferred playback settings for a particular user using a particular computer or playback device, requesting a particular multimedia file.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/431,673, filed Feb. 13, 2017, the entire disclosure of which ishereby incorporated by reference, for all purposes, as if fully setforth herein.

FIELD OF THE INVENTION

The present invention relates to automatically modifying playbacksettings of multimedia files and, more specifically, to improvedcomputer-implemented techniques for adapting playback settings based onchange history.

BACKGROUND

Typically, multimedia files are edited in post-production using astandard set of output devices. For example, the sound components of amultimedia file may be optimized using “monitor” headphones that do notadd any filters or effects to the sound profile of the file. Similarly,the video aspects of the multimedia file may be quality tested on a 60inch LCD video monitor with calibrated colors for red, blue, and green.

Unfortunately, these multimedia files are often viewed on a wide-arrayof devices that output sound and video differently. Subtle sound effectsthat are clear on monitor headphones may be completely lost when outputthrough speakers of a mobile device using an application with built inequalizers and filters. Additionally, video optimized for a 60 inch maxbrightness LCD screen may lack contrast when output through a 5.5 inchscreen of a phablet using an application with power saving featuresimplemented for that device.

During playback, when the audio or video is non-optimal, users may tryto fine-tune the playback using controls provided by the playbackapplication. While these changes may improve the playback experience forone video, the changes may actually worsen the experience for othervideos. Consequently, users who desire optimal visual and audioexperience during each video they watch may find themselves having tofiddle with setting controls before every video playback experience.

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 is a block diagram illustrating an example of a systemarchitecture of a media streaming service configured to automaticallyadjust playback settings of requested multimedia files;

FIG. 2A is a flow diagram illustrating a program flow for determiningplayback settings to automatically apply using a determinative analysis;

FIG. 2B is a flow diagram illustrating a program flow for determiningplayback settings to automatically apply using machine learning;

FIG. 3 is an example system architecture of the adaptive playbacksettings system while maintaining profiles;

FIG. 4A is an example system architecture of the adaptive playbacksettings system while performing a determinative analysis;

FIG. 4B is an example system architecture of the adaptive playbacksettings system while performing a machine learning analysis; and

FIG. 5 is a block diagram illustrating a computer system that may beused to implement the techniques described herein.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

General Overview

Techniques discussed herein improve a user's playback experience of amultimedia file by automatically adjusting playback settings based onchange history. As used herein, “Multimedia files”, “playable media”,and “streamable media” may include, among other things, audio and/orvisual media.

According to the techniques described herein, a system is configured todetect and collect information regarding changes to playback settingsmade by one or more users, and/or made in response to input from one ormore users, during presentation of a media title, such as a song and/ormovie, on one or more computers. Information may also be collected aboutthe user(s) and client devices used to playback the media. Thisinformation is collectively referred to herein as “change history data”.In one embodiment, the change history data is processed by a backendprocess on one or more server computers to determine predicted preferredplayback settings for a particular user using a particular computer orplayback device, playing a particular media title.

For example, user information and configuration information for aplurality of users may be recorded while playing a particular mediatitle. The information may be sent to a remote computer to generate oneor more machine learning models. Based on the one or more machinelearning models, a subsequent request may be analyzed in conjunctionwith one or more profiles that are associated with a predicted preferredplayback configuration for a particular media title. While a clientcomputer operated by the user presents the particular media title, theplayback configurations may be automatically adjusted throughout thepresentation based on the predicted preferred playback configurations.

System Overview

FIG. 1 is a block diagram illustrating an example of a systemarchitecture of a media streaming service configured to automaticallyadjust playback settings of requested multimedia files. The systemarchitecture 100 comprising four client devices 102, 112, 122, 132coupled to a server computer 142, which is coupled to persistentstorages 162, 172. Each of the client devices 102, 112, 122, 132comprises one or more processors 104, 114, 124, 134 and volatile memory106, 116, 126, 136 executing an instance of a video player 108, 118,128, 138 and a monitoring agent 110, 120, 130, 140. The server computer152 comprises on or more processors 154 and volatile memory 156executing an instance of a media streaming service 158 and aconfigurations manager 160.

In alternative embodiments, the system architecture 100 may comprise oneor more server computers each executing one or more instances of themedia streaming service and the configurations manager, and coupled toone or more shared persistent storage devices (e.g., hard disks, flashmemories). For example, while in the illustrated embodiment servercomputer 152 is executing a single instance of media streaming service158 and configurations manager 160, in alternative embodiments, thesystem can be scaled up vertically to execute three instances of themedia streaming service and the configurations manager or scaled uphorizontally to include three server computers each including its ownmedia streaming service and configurations manager, wherein each servercomputer is operatively coupled to the same shared disk(s).

A “client device” may be one or more physical computers, virtualcomputers, and/or computing devices. As an example, a client device maybe one or more desktop computers, laptop computers, mobile devices,smartphones, tablets, phablets, PDAs, iPods®, eReader, digital videorecorders or streamers, and/or any other special-purpose computingdevices. Any reference to “client device” herein may mean one or morecomputers, unless expressly stated otherwise.

Media streaming service instance 158 responds to requests for multimediafiles that are submitted to server computer 152 by one or more clientdevices 102, 112, 122, 132 using an instance of the video playerapplication 108, 118, 128, 138, respectively. Media streaming service158 takes each request, parses the request, and searches disk 162 forthe particular multimedia file requested. The particular multimedia filemay then be loaded into volatile memory 156 and streamed to therequesting client device. A media streaming service may contain one ormore threads to respond to requests, so multiple requests can be handledasynchronously or in-parallel.

The configurations manager hooks into the media streaming service, readseach request, and provides additional metadata to respond with. In someembodiments, the configurations manager may be referred to as a stacklayer. In these embodiments, requests are routed through theconfigurations manager to transparently provide playback settingsmanagement metadata to the client devices.

Disks 162, 172 include data and metadata of multimedia files. Disk 162contains multimedia files 164 themselves as well as indexes andstatistics on those multimedia files. Disk 172 contains profiles ofplayback settings change history data. Each profile may be stored inassociation with a respective multimedia file. In some cases, bothmultimedia files 164 and profiles 174 may be stored entirely on a singledisk.

Video Player

Video players 108, 118, 128, 138 may execute as an independentapplication or as applet in another application such as web browser. Theuser interface of a video player contains one or more controls includingphysical controls, touch screen controls, camera controls, microphonecontrols, or other input devices to control the playback of a multimediafile. Specific controls may include playing or pausing a video, jumpingto specific locations in the video or jumping ahead or behindincremental amounts of time, zooming in and out.

Video players 108, 118, 128, 138 may also contain one or more controlsallowing a user to control whether to allow the player to automaticallyadjust playback settings based on metadata received from server 152.

Adjustable Playback Settings

Adjustable playback settings may include, among other things, volume,contrast, brightness, color, gamma, color balance, resolution, size ofthe media player, and/or controlling additional screen, video and soundsettings.

Some players may contain additional configurable settings such aswhether the background behind the video player is darkened or whethersounds outside of the video player are dampened or muted. Theseadditional outside configurations may be tracked as playback settingchanges as well.

Changes to any of the above configurations, and/or any other playbacksettings may be recorded and sent to remote server computer 152 and/ordirectly to persistent storage 172. A change in a configuration maycause an event of a particular type to trigger. For example, if a clientcomputer adjusts the volume in response to user input, then aVolumeChange event may be triggered. An event may include metadata. Forexample, a VolumeChange event may include metadata such as a new volumelevel, an old volume level, and/or a difference in volume level. Theevent metadata may also include the time during playback at which thechange was applied, and a unique timestamp of the event by concatenatinga monitoring agent's unique id with the absolute time of the event, andan event id associated with the type of event.

If no playback settings are changed during the course of playback of amultimedia file, an event may be triggered at the end of the duration ofthe playback indicating that no event occurred. This “no event” metadatamay also be provided back to server computer 152.

Monitoring Agent

Monitoring agents 110, 120, 130, 140 comprise one or more threads thatgenerate metadata regarding attributes of the video playing on the videoplayer 108, 118, 128, 138; the client device 102, 112, 122, 132; orcontext that is accessible through the client device whenever a playbacksetting is adjusted. Metadata may also include information about theuser based on the user account playing the multimedia file. A monitoringagent may hook into a video player in order to generate metadata inresponse to an event triggered by the video player. Alternatively, themonitoring agent may be fully integrated into a video player, and themonitoring agent generates metadata based directly on user actions thatchange playback settings.

The attribute metadata may be streamed instantaneously to theconfigurations manager or collected and sent in discrete sized packets.Additionally, the client device may be “offline” if it is notcommunicatively coupled to one or more server computers configured toreceive user and/or configuration information. If a device is offline,the attribute metadata may be store in persistent storage of the clientdevice until the device is once again online. A client device may be“online” if it is communicatively coupled to one or more servercomputers configured to receive user and/or configuration information.

A. Device Attributes

In some embodiments, one or more monitoring agents collect deviceattributes in association with a change to playback settings event.Device attributes include but are not limited to:

-   -   Whether the changed playback setting was automatically adjusted;    -   Operating system name/version;    -   Device brand, model, and/or type (e.g.,        mobile/desktop/tablet/set top/game console, etc.);    -   Time of day    -   Geolocation;    -   Type of network (home/public/office);    -   Browser name/version; and    -   The player name/version etc.

These attributes are stored as metadata in client devices 102, 112, 122,132. The client device attributes may be detected and collected bymonitoring agents 110, 120, 130, 140, but stored as associated withchange history data by the configurations manager 160 in profiles 174.

A machine learning algorithm or a determinative categorization algorithmmay be used to group similar client devices together based on one ormore client-device attributes.

B. Media File Attributes

Media file attributes are properties of media files that may be used torelate similar content of multimedia files. For example, media fileattributes may include properties, such as file type (e.g., video/audioor more specifically, MP3, MP4, .avi, etc.), title, genre, duration,actors, keywords, tags, etc. These attributes may be stored as metadatain persistent storage 162, but stored as associated with change historydata by the configurations manager 160 in profiles 174.

A content-based filtering algorithm may be used to identify profilesthat are similar to a particular multimedia file's profile. The greaterthe similarity between the particular multimedia file and the comparedmultimedia files, the more likely playback setting change events thatoccurred during the compared multimedia files would also be preferredduring the particular multimedia file. The content-based filteringalgorithm may be a machine learning algorithm trained with a trainingset of similar and different multimedia files.

C. User Attributes

User attributes are any inputs that relate to user opinions or behavior.User attributes take advantage of the assumption that two people thathave similar opinions about one thing are likely to have similaropinions about another thing. In the context of predicting playbacksetting changes during playback of multimedia files, user attributesassume that if a target user has performed similar changes to playbacksettings as another user, then the target user is likely to make thesame changes to playback settings that the other user made.

Information about the opinions of users may be obtained or derived in avariety of ways. For example, the types of changes a user has made isimplicitly reflected in the user's profile history. Thus, userattributes may include metadata about the video viewing history of thetarget user, the viewing history of users that are similar to the targetuser, and/or the viewing history of an entire population of viewers.

User attributes may also include demographic information, based on theassumption that users with similar demographic characteristics will havesimilar playback settings changes. Such demographic information mayinclude, for example, the age, gender, and geographic location of users.The user attributes may also include interests that users haveexplicitly identified by, for example, filling out a video preferencesurvey or rating previously-viewed videos.

Playback Settings Manager

The configurations manager 160 processes newly received change historydata to derive improved predicted playback configuration(s) for a betterplayback experience. The server computer(s) may process the newlyreceived information as the information is received, and may store theplayback configuration(s) in profiles 174 associated with individualmultimedia files, user accounts, or client devices. Additionally oralternatively, configurations manager 160 may process newly receivedinformation periodically and/or at regular intervals, such as every 15,30, 60 minutes, and/or once every day, etc. Additionally oralternatively, configurations manager 160 may process raw change historydata in a multimedia profile when a request for a particular multimediafile is received.

The configurations manager may employ one or more conditionalexpressions, statistical models, or machine learning techniques and/ormodels to predict playback setting changes to automatically apply forfuture requests of a multimedia file.

Example Detection Case

FIG. 3 is an example system architecture of the adaptive playbacksettings system while maintaining profiles. The example systemarchitecture delineates occurrences 302, 304, 306, 308 of Video A beingplayed on each respective instance 108, 118, 128, 138 of a video playerrunning on a respective client device 102, 112, 122, 132. As eachoccurrence 302, 304, 306, 308 of Video A is played, a respectivemonitoring agent 110, 120, 130, 140 detects changes to playback settingsas an event generated by the respective video player 108, 118, 128, 138,and collects attributes from the respective client device 102, 112, 122,132 regarding the client device, the client device context, the useraccount playing Video A on that instance, Video A itself, or anycombination thereof. As indicated by the arrows 312, 314, 316, 318, thechanges are sent to remote configurations manager 160, which logs thechanges in one or more profiles including profile 322 as change historydata.

The configurations manager 160 logs the change history data with an IDor timestamp and proceeds to store the data in one or more correspondingprofiles of profiles 174. The change history data of profile 322 showsfour records of change history data that were logged based on eventstriggered from four client devices 102, 112, 122, 132. The recordscontain the time of the change during playback of the video, the devicetype of the client device that received the change, and the actual eventthat occurred. In this particular example, an increase in volume wasdetected around the same time (˜2:06) during playback of Video A onclient devices 102, 112, 122. No increase in volume was detected inclient device 132. In this particular example, device-type iscategorized based on operating system of the client device. Three clientdevices run the iOS® operating system, and one client device runs anAndroid® operating system.

Determinitative Analysis Overview

FIG. 2A is a flow diagram illustrating a program flow for determiningplayback settings to automatically apply using a determinative analysis.At step 202, a configurations manager, which is executing on a servercomputer, maintains a respective profile of changes to playback settingsfor each of a plurality of multimedia files. As seen at step 204,maintaining these profiles may include receiving change history datathat is based on the changes to the playback settings that were detectedto have occurred during playback of the respective multimedia file. Asseen at step 206, maintaining these profiles may further include storingthe change history data as a record in a profile for the multimediafile. Arrow 208 indicates that steps 204 may need to execute multipletimes. Each time more change history data is received and stored in aprofile.

At step 210, the configurations manager receives from a particularclient device, a request for a particular multimedia file. Based on theprofile of the particular multimedia file, the configurations managerdetermines the changes to apply to playback settings for playback of theparticular multimedia file at step 212. As seen at step 214, determiningthe changes to apply may include the manager identifying, from thechange history data of the profile, the changes that had been previouslyapplied to the multimedia file during playback. Identifying may involvegrouping records in the profile by when they were applied duringplayback and further grouping them by which device type was used whenthey were applied. Then a set of conditions are applied to those groupsof records to determine whether each change should be appliedautomatically during playback at step 216. A simple condition could beif a change is logged in more than 500 records that are grouped bydevice type, then that change will be applied automatically duringplayback. Arrow 218 indicates that multiple changes to playback settingsmay be identified at step 214, so the threshold conditions are appliedto each change of the multiple changes to determine which changes shouldbe applied automatically.

At step 220, the manager responds to the request by causing themultimedia file to play, using a particular application on the clientdevice, with the one or more changes to the playback settings determinedat step 212. As seen at step 222, causing the changes to be applied mayinclude providing, to the client device, each change that meets thethreshold conditions in addition to providing the multimedia fileitself. The instance of the video player application on the clientdevice is configured to automatically apply these changes duringplayback of the particular multimedia file as seen at step 224.

Determinative Analysis Example

FIG. 4A is an example system architecture of the adaptive playbacksettings system while performing a determinative analysis. Client device102 sends a request 402 for video A to server computer 152. Remoteconfigurations manager 160 reads the four records in profile 322 andgroups them by playback settings changed at a particular time duringplayback and by specific device-type. The particular time and action canbe grouped based on statistical significance such as times recordedwithin a standard deviation of one another. Thus, the volume changes 21,25 can all be grouped in an average volume category of 23 and, the timesare grouped by an average time 2:06.5. The specific device-type can begrouped based on any number of factors such as iOS type and version ormodel of device.

Once the devices are grouped, the configurations manager applies a setof one or more conditions to the aggregate number (#) of changes toplayback settings grouped in each category. In the example 400, thecondition is that the aggregate number of changes must be greater thanor equal to two AND the population to enact said change must be greaterthan 50%. Based on these conditions, a change in volume willautomatically be applied to client devices requesting Video A that arerunning an iOS operating system (i.e., aggregate number of devices(2)>=2AND aggregate percentage (⅔˜66%)>50%), but the changes will not beapplied to a device running an android operating system (i.e. aggregatenumber of devices(1) IS NOT>=2).

Assume client device 102 is running an iOS operating system. The requestfor video A is responded to with Video A and playback settings includingincreasing the volume by 23.3 at 2:06 during playback. Video player 108plays Video A with these changes to playback settings automaticallyapplied at the appropriate time (2:06).

Machine Learning Analysis Overview

FIG. 2B is a flow diagram illustrating a program flow for determiningplayback settings to automatically apply using machine learning. At step252, a configurations manager, which is executing on a server computer,maintains multiple profiles of changes to playback settings for each ofa plurality of multimedia files. As seen at step 254, maintaining theseprofiles may include receiving change history data that is based on thechanges to the playback settings that were detected to have occurredduring playback of the respective multimedia file. As seen at step 256,maintaining these profiles may further include storing the changehistory data as a record in a profile for the multimedia file. Arrow 258indicates that steps 204 may need to execute multiple times. Each timechange history data is received, more change history data is stored inone or more corresponding profiles.

At step 260, the configurations manager receives from a particularclient device, a request for a particular multimedia file. Based on oneor more profiles associated with the particular multimedia file, themanager determines the changes to apply to playback settings forplayback of the particular multimedia file at step 262. As seen at step264, determining the changes to apply may include the managerdetermining a set of associated profiles. These profiles may beassociated based on similar attributes to the attributes of the useraccount, client device, or multimedia file of the request. For example,the request for the particular multimedia file may have attributes suchas “Genre:Action”, “time_of_day:6:00 pm”, and “geolocation: SanFrancisco.” Profiles with change history data of events that occurred tomedia files of the same genre, during the same time of day, or in asimilar geographic region may be identified as associated with therequest. The remote configurations manager then applies a machinelearning algorithm to a set of one or more attributes associated withthe request for the particular multimedia file using the attributes andactions in the change history data of the one or more profiles. Themachine learning algorithm compares the attributes associated with therequest to similar attributes that are recorded in the change historydata of the one or more profiles. At step 266, the configurationsmanager then generates one or more scores for one or more possiblechanges to playback settings. Some changes to playback settings that arein the profile of the particular multimedia file may naturally beweighted more heavily using the machine learning algorithm. The machinelearning algorithm may be trained to weigh similarities between theseattributes more heavily.

In some embodiments, scores may only be given to possible changes thatare at least derived from a set of changes recorded in the changehistory data of the profile of the particular multimedia file that wasrequested. Alternatively, the scores may be given to possible changesrecorded in the change history data of all of the one or more profilesthat were selected based on similar requests.

The configurations manager uses the machine learning algorithm togenerate a score for every possible change, and then at step 268, theconfigurations manager selects the changes to automatically apply to therequested multimedia file based on the scores. For example, theconfigurations manager may apply a threshold score condition of (0.7) orhigher in order to select changes to automatically apply.

At step 220, the manager responds to the request by causing themultimedia file to play, using a particular application on the clientdevice, with the selected one or more changes to the playback settingsdetermined at step 262. As seen at step 272, causing the changes to beapplied may include providing, to the client device, each change thatmeets the threshold score in addition to providing the multimedia fileitself. The instance of the video player application on the clientdevice is configured to automatically apply these changes duringplayback of the particular multimedia file as seen at step 274.

Machine Learning Analysis Example

FIG. 4B is an example system architecture of the adaptive playbacksettings system while performing a machine learning analysis. Clientdevice 102 sends a request 452 for Video A with attributes to servercomputer 152. Remote configurations manager 160 reads request 452 andparses out client device attributes 462, requested movie attributes 464,and the user account attributes 466. The configurations manager 160selects a group of one more profiles with similar attributes toattributes 462, 464, 464. The configurations manager 160 then usesmachine learning engine 470 to apply the change history data from theseprofiles to the client device attributes 462, requested media fileattributes 464, and user attributes 466 to generate scores for each of aplurality of possible changes to automatically apply to playbacksettings. The possible changes are derived from one or more relatedprofiles in profiles 472, 474, 476 based on the attributes of theparticular multimedia file (i.e., Video A) requested by the particularuser account (not shown) using the particular client device 106. In thisexample, the possible changes include:

-   -   Volume change 1 at time X,    -   Volume change 2 at time Y, and    -   Contrast change 3 at time Z

The machine learning engine 470 generates, for each of the possiblechanges listed above, a respective score 482, 484, 486.

Once the scores are generated, the configurations manager 160 applies athreshold score condition to each of the generated scores 482, 484, 486and then causes the video player 108 to automatically apply changes toplayback settings based on scores that meet the threshold scorecondition.

Assume the threshold score condition is 0.7 and the volume change 1score 482 meets this condition. The request for Video A is responded towith Video A and playback settings including changing the volume tovolume change 1 at time X during playback. Video player 108 plays VideoA with these changes to playback settings automatically applied at theappropriate time.

Assume the contrast change 3 score 486 also meets the threshold scorecondition. This may be for reasons unrelated to the media file profiles474. For example, the particular geolocation may be located in a ruralarea and the time of day of the client device is at night—bothattributes stored in the change history data of client device profiles472. Requests from client devices with these types of attributes willautomatically cause changes to all multimedia files played on thesedevices if the machine learning engine is given enough information todecrease the weight of the attributes in the media file profiles 474.

Client Device Feedback

Machine learning engine 470 can “learn” from further changes to playbacksettings recorded after automatically applying changes to playbacksettings. Thus, if monitoring agent 110 detects additional playbacksettings changes after some playback changes were automatically applied,the profile of Video A, the user profile of the particular user accountplaying the multimedia file, and the profile for the client device maybe changed accordingly.

Changes made to playback settings that are directly related to aparticular profile that matches the attributes sent in the request(i.e., same user profile, same media file, or same client deviceprofile) may be weighted more heavily by the machine learning engine.Weighting the attributes of a particular profile may be done manually bymaking a separate category of attributes that are weighted heavier or byhaving multiple copies of the particular profiles that match input intothe machine learning engine.

Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 5 is a block diagram that illustrates a computersystem 500 upon which an embodiment of the invention may be implemented.Computer system 500 includes a bus 502 or other communication mechanismfor communicating information, and a hardware processor 504 coupled withbus 502 for processing information. Hardware processor 504 may be, forexample, a general purpose microprocessor.

Computer system 500 also includes a main memory 506, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 502for storing information and instructions to be executed by processor504. Main memory 506 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 504. Such instructions, when stored innon-transitory storage media accessible to processor 504, rendercomputer system 500 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 500 further includes a read only memory (ROM) 508 orother static storage device coupled to bus 502 for storing staticinformation and instructions for processor 504. A storage device 510,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 502 for storing information and instructions.

Computer system 500 may be coupled via bus 502 to a display 512, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 514, including alphanumeric and other keys, is coupledto bus 502 for communicating information and command selections toprocessor 504. Another type of user input device is cursor control 516,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 504 and forcontrolling cursor movement on display 512. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 500 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 500 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 500 in response to processor 504 executing one or more sequencesof one or more instructions contained in main memory 506. Suchinstructions may be read into main memory 506 from another storagemedium, such as storage device 510. Execution of the sequences ofinstructions contained in main memory 506 causes processor 504 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 510. Volatile media includes dynamic memory, such asmain memory 506. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 502. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 504 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 500 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 502. Bus 502 carries the data tomain memory 506, from which processor 504 retrieves and executes theinstructions. The instructions received by main memory 506 mayoptionally be stored on storage device 510 either before or afterexecution by processor 504.

Computer system 500 also includes a communication interface 518 coupledto bus 502. Communication interface 518 provides a two-way datacommunication coupling to a network link 520 that is connected to alocal network 522. For example, communication interface 518 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 518 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 518sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 520 typically provides data communication through one ormore networks to other data devices. For example, network link 520 mayprovide a connection through local network 522 to a host computer 524 orto data equipment operated by an Internet Service Provider (ISP) 526.ISP 526 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 528. Local network 522 and Internet 528 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 520and through communication interface 518, which carry the digital data toand from computer system 500, are example forms of transmission media.

Computer system 500 can send messages and receive data, includingprogram code, through the network(s), network link 520 and communicationinterface 518. In the Internet example, a server 530 might transmit arequested code for an application program through Internet 528, ISP 526,local network 522 and communication interface 518.

The received code may be executed by processor 504 as it is received,and/or stored in storage device 510, or other non-volatile storage forlater execution.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

What is claimed is:
 1. A method comprising: during playback of each of aplurality of multimedia files, detecting changes to playback settings;for each respective multimedia file of the plurality of multimediafiles, maintaining a respective profile that includes change historydata that is based on the changes to the playback settings that weredetected to have occurred during playback of the respective multimediafile; receiving, from a particular client device, a request for aparticular multimedia file of the plurality of multimedia files, whereinthe request includes a set of one or more attributes associated with therequest; based on change history data from one or more profiles of theplurality of multimedia files, determining one or more changes to applyto playback settings for playback of the particular multimedia file,wherein: the one or more changes to apply to playback settings includeapplying machine learning algorithm to the set of one or more attributesassociated with the request for the particular multimedia file, and theone or more changes are generated based on the machine learningalgorithm; and responding to the request by causing the multimedia fileto play, using a particular application on said particular clientdevice, with said one or more changes to the playback settingsautomatically applied during playback; wherein the method is performedby one or more computing devices.
 2. The method of claim 1, wherein theone or more profiles includes at least a particular profile of changes,by viewers, to playback settings of the particular multimedia file;wherein determining the one or more changes to the playback settingsbased on the one or more profiles of the plurality of multimedia files,includes: grouping the changes of the particular profile by: a time atwhich each change occurred while playing the particular multimedia file,and a client device type of a plurality of client device types on whicheach change to the playback settings occurred; wherein determining theone or more changes to the playback settings includes applying a set ofone or more threshold conditions to the groupings.
 3. The method ofclaim 2, wherein the one or more changes to playback settings includes achange to volume while playing a particular multimedia file at aparticular time on a particular client device type that is the clientdevice type of the particular client device.
 4. The method of claim 1,further comprising: for each client computer of a plurality of clientcomputers, receiving attributes associated with each change to theplayback settings; wherein determining the one or more changes to theplayback settings based on the one or more profiles of the plurality ofmultimedia files, includes: generating, for the particular multimediafile, a respective score for each possible change to playback settingsdescribed in the one or more profiles; and based on one or more scoresgenerated, selecting the one or more changes to the playback settings.5. The method of claim 4, wherein the one or more profiles includes atleast a particular profile of changes, by viewers, to playback settingsof the particular multimedia file; wherein the method further comprises:increasing a weight of attributes associated with changes, by viewers,to playback settings of the particular multimedia file.
 6. The method ofclaim 4, wherein the attributes include time of day and geolocation. 7.The method of claim 6, wherein the one or more changes to playbacksettings includes a change to contrast during a particular time of dayat a particular geolocation.
 8. One or more non-transitorycomputer-readable media storing one or more sequences of instructionswhich, when executed by one or more processors, cause performance of:during playback of each of a plurality of multimedia files, detectingchanges to playback settings; for each respective multimedia file of theplurality of multimedia files, maintaining a respective profile thatincludes change history data that is based on the changes to theplayback settings that were detected to have occurred during playback ofthe respective multimedia file; receiving, from a particular clientdevice, a request for a particular multimedia file of the plurality ofmultimedia files, wherein the request includes a set of one or moreattributes associated with the request; based on change history datafrom one or more profiles of the plurality of multimedia files,determining one or more changes to apply to playback settings forplayback of the particular multimedia file, wherein: the one or morechanges to apply to playback settings include applying machine learningalgorithm to the set of one or more attributes associated with therequest for the particular multimedia file, and the one or more changesare generated based on the machine learning algorithm; and responding tothe request by causing the multimedia file to play, using a particularapplication on said particular client device, with said one or morechanges to the playback settings automatically applied during playback.9. The one or more non-transitory computer-readable media of claim 8,wherein the one or more profiles includes at least a particular profileof changes, by viewers, to playback settings of the particularmultimedia file; wherein determining the one or more changes to theplayback settings based on the one or more profiles of the plurality ofmultimedia files, includes: grouping the changes of the particularprofile by: a time at which each change occurred while playing theparticular multimedia file, and a client device type of a plurality ofclient device types on which each change to the playback settingsoccurred; wherein determining the one or more changes to the playbacksettings includes applying a set of one or more threshold conditions tothe groupings.
 10. The one or more non-transitory computer-readablemedia of claim 9, wherein the one or more changes to playback settingsincludes a change to volume while playing a particular multimedia fileat a particular time on a particular client device type that is theclient device type of the particular client device.
 11. The one or morenon-transitory computer-readable media of claim 8, wherein the one ormore non-transitory computer-readable media store instructions which,when executed by one or more processors, further cause: for each clientcomputer of a plurality of client computers, receiving attributesassociated with each change to the playback settings; whereindetermining the one or more changes to the playback settings based onthe one or more profiles of the plurality of multimedia files, includes:generating, for the particular multimedia file, a respective score foreach possible change to playback settings described in the one or moreprofiles; and based on one or more scores generated, selecting the oneor more changes to the playback settings.
 12. The one or morenon-transitory computer-readable media of claim 11, wherein the one ormore profiles includes at least a particular profile of changes, byviewers, to playback settings of the particular multimedia file; whereinthe one or more non-transitory computer-readable media storeinstructions which, when executed by the one or more processors, furthercause: increasing a weight of attributes associated with changes, byviewers, to playback settings of the particular multimedia file.
 13. Theone or more non-transitory computer-readable media of claim 11, whereinthe attributes include time of day and geolocation.
 14. The one or morenon-transitory computer-readable media of claim 13, wherein the one ormore changes to playback settings includes a change to contrast during aparticular time of day at a particular geolocation.
 15. An adaptiveplayback management system comprising one or more computing devicesconfigured to: during playback of each of a plurality of multimediafiles, detect, by a monitoring agent, changes to playback settings; foreach respective multimedia file of the plurality of multimedia files,maintain, by a configuration manager, a respective profile that includeschange history data that is based on the changes to the playbacksettings that were detected to have occurred during playback of therespective multimedia file; receive, from a particular client device, arequest for a particular multimedia file of the plurality of multimediafiles, wherein the request includes a set of one or more attributesassociated with the request; based on change history data from one ormore profiles of the plurality of multimedia files, determine one ormore changes to apply to playback settings for playback of theparticular multimedia file, wherein: the one or more changes to apply toplayback settings include applying machine learning algorithm to the setof one or more attributes associated with the request for the particularmultimedia file, and the one or more changes are generated based on themachine learning algorithm; and respond to the request by causing themultimedia file to play, using a particular application on saidparticular client device, with said one or more changes to the playbacksettings automatically applied during playback.
 16. The adaptiveplayback management system of claim 15, wherein the one or more profilesincludes at least a particular profile of changes, by viewers, toplayback settings of the particular multimedia file; wherein in order todetermine the one or more changes to the playback settings based on theone or more profiles of the plurality of multimedia files, the one ormore computing devices are further configured to: grouping the changesof the particular profile by: a time at which each change occurred whileplaying the particular multimedia file, and a client device type of aplurality of client device types on which each change to the playbacksettings occurred; wherein determining the one or more changes to theplayback settings includes applying a set of one or more thresholdconditions to the groupings.
 17. The adaptive playback management systemof claim 16, wherein the one or more changes to playback settingsincludes a change to volume while playing a particular multimedia fileat a particular time on a particular client device type that is theclient device type of the particular client device.
 18. The adaptiveplayback management system of claim 15, wherein the one or morecomputing devices are further configured to: for each client computer ofa plurality of client computers, receive attributes associated with eachchange to the playback settings; wherein the configuration to determinethe one or more changes to the playback settings based on the one ormore profiles of the plurality of multimedia files, includes: generate,for the particular multimedia file, a respective score for each possiblechange to playback settings described in the one or more profiles; andbased on one or more scores generated, selecting the one or more changesto the playback settings.
 19. The adaptive playback management system ofclaim 18, wherein the one or more profiles includes at least aparticular profile of changes, by viewers, to playback settings of theparticular multimedia file; wherein the one or more computing devicesare further configured to: increase a weight of attributes associatedwith changes, by viewers, to playback settings of the particularmultimedia file.
 20. The adaptive playback management system of claim18, wherein the attributes include time of day and geolocation.